Building Trustworthy AI: A Blueprint for Secure Agents

Key points

• Trust is becoming a major challenge for enterprise AI, with companies needing to ensure their AI systems are safe, secure, and under control as they scale.
• A layered approach to trust is necessary, combining identity, guardrails, evaluations, adversarial testing, data protection, monitoring, and governance to build trustworthy AI agents.
• Azure AI Foundry provides a range of tools and capabilities to support this approach, including unique agent IDs, built-in controls, risk and safety evaluations, and data protection features.

As the use of AI agents becomes more widespread in enterprises, trust is rapidly becoming a major challenge. Companies are looking for ways to ensure their AI systems are safe, secure, and under control as they scale. This is not just a matter of applying a patchwork of point fixes, but rather requires a blueprint for trust that combines multiple elements to build trustworthy AI agents.

According to sources, Chief Information Security Officers (CISOs) are worried about agent sprawl and unclear ownership, while security teams need guardrails that connect to their existing workflows. Developers, on the other hand, want safety built in from day one, not added at the end. These pressures are driving the shift left phenomenon, where security, safety, and governance responsibilities are moving earlier into the developer workflow.

To build trustworthy AI agents, five key qualities stand out: unique identity, data protection by design, built-in controls, evaluated against threats, and continuous oversight. These qualities do not guarantee absolute safety, but they are essential for meeting enterprise standards. Microsoft’s approach to trustworthy AI involves baking these qualities into their products, with protections layered across the model, system, policy, and user experience levels.

Azure AI Foundry is a key tool in supporting this approach, providing a range of capabilities to help enterprises build trustworthy AI agents. These include unique Entra Agent IDs, which give organizations visibility into all active agents across a tenant and help reduce shadow agents. Agent controls are also available, including a cross-prompt injection classifier that scans for malicious instructions and flags, blocks, and neutralizes them.

In addition, Azure AI Foundry provides risk and safety evaluations, which give teams a feedback loop across the lifecycle of their AI agents. These evaluations include harm and risk checks, groundedness scoring, and protected material scans, both before deployment and in production. Data control is also a key feature, with standard agent setup allowing enterprises to bring their own Azure resources and keep data processed by agents within the tenant’s boundary.

Network isolation is another important feature, with private network isolation and custom virtual networks and subnet delegation ensuring that agents operate within a tightly scoped network boundary. Microsoft Purview also helps extend data security and compliance to AI workloads, with agents in Foundry able to honor Purview sensitivity labels and DLP policies.

As Microsoft continues to develop and refine Azure AI Foundry, it is clear that trust is a top priority. By providing a range of tools and capabilities to support a layered approach to trust, Azure AI Foundry is helping enterprises build trustworthy AI agents that automate tasks, enhance user experiences, and deliver results. With Azure AI Foundry, companies can create a blueprint for trust that ensures their AI systems are safe, secure, and under control as they scale. Microsoft’s commitment to trustworthy AI is evident in the development of Azure AI Foundry, and it will be interesting to see how this tool continues to evolve and support the growth of enterprise AI.

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